We propose instead an event-only approach to predict skeletal poses from a single stream of events (Figure REF ). Our pipeline consists of two steps. First, a Convolutional Neural Network predicts the projection of each joint of the skeleton onto three orthogonal planes. Instead of predicting the positions directly, we constrain our approach onto estimating intermediate heatmaps of probabilities for each joint. Second, we triangulate the sets of 2D positions of each joint to predict the 3D joint pose.
Similar workss adopt raw events to solve pose estimation tasks [1]}, [2]}. On the other hand, we aggregate events into tensor-like representations. Although event-representations have been widely investigated and validated [3]}, [4]}, [5]}, [6]}, no previous work has explored these approaches for monocular human pose estimation. Moreover, differently from standard computer vision, where transfer learning across different tasks has been widely investigated [7]}, it is still unclear whether pre-training on related tasks can improve event-based human pose estimation. To fill these gaps, we compare different pre-training tasks and different event-representations.
